✦ LIBER ✦

A hybrid filter/wrapper approach of feature selection using information theory

✍ Scribed by Marc Sebban; Richard Nock

Publisher: Elsevier Science
Year: 2002
Tongue: English
Weight: 138 KB
Volume: 35
Category: Article
ISSN: 0031-3203
DOI: 10.1016/s0031-3203(01)00084-x

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✦ Synopsis

We focus on a hybrid approach of feature selection. We begin our analysis with a ÿlter model, exploiting the geometrical information contained in the minimum spanning tree (MST) built on the learning set. This model exploits a statistical test of relative certainty gain, used in a forward selection algorithm. In the second part of the paper, we show that the MST can be replaced by the 1 nearest-neighbor graph without challenging the statistical framework. This leads to a feature selection algorithm belonging to a new category of hybrid models (ÿlter-wrapper). Experimental results on readily available synthetic and natural domains are presented and discussed.

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